Sweat sensing technologies have enormous potential for applications ranging from athletics, to neonates, to pharmacological monitoring, to personal digital health, to name a few applications. Sweat contains many of the same biomarkers, chemicals, or solutes that are carried in blood and can provide significant information enabling one to diagnose ailments, health status, toxins, performance, and other physiological attributes even in advance of any physical sign. Furthermore, sweat itself, the action of sweating, and other parameters, attributes, solutes, or features on, near, or beneath the skin can be measured to further reveal physiological information.
If sweat has such significant potential as a sensing paradigm, then why has it not emerged beyond decades-old usage in infant chloride assays for Cystic Fibrosis or in illicit drug monitoring patches? In decades of sweat sensing literature, the majority of medical literature utilizes the crude, slow, and inconvenient process of sweat stimulation, collection of a sample, transport of the sample to a lab, and then analysis of the sample by a bench-top machine and a trained expert. This process is so labor intensive, complicated, and costly that in most cases, one would just as well implement a blood draw since it is the gold standard for most forms of high performance biomarker sensing. Hence, sweat sensing has not emerged into its fullest opportunity and capability for biosensing, especially for continuous or repeated biosensing or monitoring. Furthermore, attempts at using sweat to sense “holy grails” such as glucose have not yet succeeded to produce viable commercial products, reducing the publically perceived capability and opportunity space for sweat sensing.
Products on the market, such as one-time Cystic Fibrosis testing devices, or continuous sweat sampling and sensing devices, fail to provide chronological assurance, which is an assurance of the sampling rate for measurement(s) of sweat or solutes in sweat in terms of the rate at which measurements can be made of new sweat or its new solutes as originating from the body. Simple one-time sampling products exist where the only critical parameter is to collect an adequate sample for transfer to a chloride sensor and to preserve the sweat volume (little or no evaporation) to prevent changes in concentration of chloride in sweat. Glucose sensors may use a “fixed volume reservoir” to obtain a precise volume of sweat, which can then ensure adequate sample and to provide a more accurate determination of glucose concentration. Devices intended to test for Cystic Fibrosis in neonates, who provide very little sweat for a sample, can include a sweat generation rate measurement and a digital display of time elapsed to indicate when proper sample volume is achieved. These “continuous monitoring” devices are capable of assuring continuous sampling and reading, but not chronological assurance. This inability to provide chronological assurance is a major deficiency for many applications possible for sweat sensing.
Of all the other physiological fluids used for bio monitoring (e.g. blood, urine, saliva, tears), sweat has arguably the most variable sampling rate as its collection methods and variable rate of generation both induce large variances in the effective sampling rate. Sweat is also exposed to numerous contamination sources, which can distort the effective sampling rate. The variable sampling rate creates a challenge in providing chronological assurance, especially so in continuous monitoring applications.
For example, consider the difficulty of sampling sweat in a sweat sensing patch with a large sweat volume that could mix up sweat previously generated with the newly generated sweat that is intended to be measured to represent a measurement of sweat solutes in real time or near real time. Such need for chronological assurance is largely unique to sweat. Furthermore, even technologies useful for chronological assurance with other biofluids could be largely irrelevant as they do not work with the unique signatures of sweat and of sweat sensors that could allow for chronological assurance. Techniques exist that reduce the sweat volume, but reducing the sweat volume does not enable an understanding of how the sweat sampling rate changes with sweat volume or movement of sweat fluid or solutes between the sensors and the skin, due to diffusion, and due to sweat or flow rates. There is a clear difference between merely improving sweat volume or sweat sampling rate and providing chronological assurance.
A sweat sensor with chronological assurance is clearly needed. A continuously monitoring or one time sweat sensor might give you a biomarker reading, but if it does not tell the window over which that biomarker collection is integrated, then the reading is useless for numerous applications. For example, consider athlete monitoring during a game, the coach would want to know if the readings of fatigue on a particular athlete represent 5 minute chronological assurance or 50 minute chronological assurance. Furthermore, some biomarkers disappear from sweat in as little as 10 to 20 minutes, and an assurance that chronological readings are less than 5 to 10 minutes would be needed.
Many of the drawbacks stated above can be resolved by creating novel and advanced interplays of chemicals, materials, sensors, electronics, microfluidics, algorithms, computing, software, systems, and other features or designs, in a manner that affordably, effectively, conveniently, intelligently, or reliably brings sweat sensing technology into intimate proximity with sweat as it is generated. With such a new invention, sweat sensing could become a compelling new paradigm as a biosensing platform.